Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems

Original Paper: https://arxiv.org/abs/2407.01370

By: Philippe Laban, Alexander R. Fabbri, Caiming Xiong, Chien-Sheng Wu

Abstract:

LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like Needle-in-a-Haystack lack complexity. In this work, we argue that summarization can play a central role in such evaluation. We design a procedure to synthesize Haystacks of documents, ensuring that specific \textit{insights} repeat across documents. The "Summary of a Haystack" (SummHay) task then requires a system to process the Haystack and generate, given a query, a summary that identifies the relevant insights and precisely cites the source documents. Since we have precise knowledge of what insights should appear in a haystack summary and what documents should be cited, we implement a highly reproducible automatic evaluation that can score summaries on two aspects - Coverage and Citation. We generate Haystacks in two domains (conversation, news), and perform a large-scale evaluation of 10 LLMs and corresponding 50 RAG systems. Our findings indicate that SummHay is an open challenge for current systems, as even systems provided with an Oracle signal of document relevance lag our estimate of human performance (56\%) by 10+ points on a Joint Score. Without a retriever, long-context LLMs like GPT-4o and Claude 3 Opus score below 20% on SummHay. We show SummHay can also be used to study enterprise RAG systems and position bias in long-context models. We hope future systems can equal and surpass human performance on SummHay.

Summary Notes

Figure: Diagram illustrating the steps to synthesize a Haystack of documents given an input scenario: subtopic and insight creation followed by document generation. Once a Haystack is synthesized, it can be used to benchmark LLMs / RAG systems on query-focused summarization tasks.

As engineers, we constantly push the boundaries of what's possible with technology, and one area that's seen significant progress is the handling of extensive text inputs by Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. These systems can now process millions of tokens, making them extremely powerful tools for tasks requiring vast amounts of data. However, evaluating their effectiveness in long-context tasks remains a complex challenge. This blog post delves into a recent study that addresses this issue with an innovative benchmarking task: the "Summary of a Haystack" (SummHay).

Introduction to SummHay

The SummHay benchmark is designed to rigorously test the capabilities of LLMs and RAG systems in summarizing long-context documents. Traditional tasks like Needle-in-a-Haystack, which involve finding a small piece of information in a large document, are too simplistic to differentiate among the latest models. SummHay, on the other hand, requires models to process a large collection of documents (the "Haystack") and generate a summary that identifies key insights and accurately cites source documents.

Methodology

Data Synthesis

The researchers created synthetic Haystacks of documents in two domains: conversations and news articles. Each Haystack contains around 100 documents, totaling approximately 100k tokens. These documents are populated with specific insights that repeat across multiple documents. The task for the models is to summarize these insights accurately and provide precise citations.

Evaluation Metrics

The evaluation of SummHay focuses on two main aspects:

1. Coverage: This measures how well the generated summary covers the expected insights.
2. Citation: This assesses the precision and recall of the citations in the summary, ensuring that the sources are accurately cited.

The final score, called the Joint Score, combines these metrics to provide a holistic assessment of the model's performance.

Key Findings

The researchers evaluated 10 LLMs and 50 corresponding RAG systems on the SummHay benchmark. Here are some of the most notable findings:

1. Performance Gap: Even the best-performing systems lagged behind human performance by more than 10 points in the Oracle setting, indicating a significant gap that needs to be bridged.
2. Retrieval Matters: RAG systems that used advanced retrieval techniques, like Cohere's Rerank3, showed improved performance in citation quality, although they often compromised on coverage.
3. Position Bias: The study confirmed that most LLMs exhibit a position bias, favoring information at the top or bottom of the context window, which impacts their summarization accuracy.

Implications and Applications

The SummHay benchmark provides a robust framework for evaluating long-context LLMs and RAG systems. Here are a few potential applications and implications of this research:

1. Improving Enterprise Search: By leveraging advanced RAG components, enterprises can develop more reliable and accurate search engines that can handle vast amounts of data.
2. Enhancing Content Management: Organizations dealing with large documents can use these models to generate summaries, making it easier to extract key insights and manage content more effectively.
3. Academic Research: Researchers can use the SummHay benchmark to develop and test new models, pushing the boundaries of what’s possible in natural language processing.

Conclusion

The SummHay benchmark is a significant step forward in evaluating the capabilities of long-context LLMs and RAG systems. While current models show promise, there is still a considerable gap between their performance and human-level summarization. By focusing on both coverage and citation accuracy, SummHay provides a comprehensive metric for future advancements. As we continue to develop more sophisticated models, benchmarks like SummHay will be crucial in guiding our progress and ensuring that these models can handle the complexities of real-world applications.

So, next time you're faced with a "haystack" of documents, remember that while our current tools are powerful, there's still a lot of room for improvement. And that's where the future of AI and machine learning gets exciting.